2 research outputs found
Expression Empowered ResiDen Network for Facial Action Unit Detection
The paper explores the topic of Facial Action Unit (FAU) detection in the
wild. In particular, we are interested in answering the following questions:
(1) how useful are residual connections across dense blocks for face analysis?
(2) how useful is the information from a network trained for categorical Facial
Expression Recognition (FER) for the task of FAU detection? The proposed
network (ResiDen) exploits dense blocks along with residual connections and
uses auxiliary information from a FER network. The experiments are performed on
the EmotionNet and DISFA datasets. The experiments show the usefulness of
facial expression information for AU detection. The proposed network achieves
state-of-art results on the two databases. Analysis of the results for cross
database protocol shows the effectiveness of the network
Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition
Recognizing the expressions of partially occluded faces is a challenging
computer vision problem. Previous expression recognition methods, either
overlooked this issue or resolved it using extreme assumptions. Motivated by
the fact that the human visual system is adept at ignoring the occlusion and
focus on non-occluded facial areas, we propose a landmark-guided attention
branch to find and discard corrupted features from occluded regions so that
they are not used for recognition. An attention map is first generated to
indicate if a specific facial part is occluded and guide our model to attend to
non-occluded regions. To further improve robustness, we propose a facial region
branch to partition the feature maps into non-overlapping facial blocks and
task each block to predict the expression independently. This results in more
diverse and discriminative features, enabling the expression recognition system
to recover even though the face is partially occluded. Depending on the
synergistic effects of the two branches, our occlusion-adaptive deep network
significantly outperforms state-of-the-art methods on two challenging
in-the-wild benchmark datasets and three real-world occluded expression
datasets